Ventricular fibrillation detection by autocorrelation function peak analysis
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Updated: Oct 23, 2026

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation
Published on: July 29, 2011
S G Guillén1, M T Arredondo, G Martin
1Department of Electronics, Universidad Politécnica de Valencia, Spain.
Unlabelled:
The use of reliable automatic ventricular fibrillation (VF) recognition techniques is critical in performing external automatic defibrillation. The authors' objective was to develop a method to detect VF and life-threatening arrhythmias, based on direct and simple peak analysis of the autocorrelation function (ACF). This method may differentiate between fibrillating and nonfibrillating rhythms, and in the first case between "course" and "fine" VF. ECG records during ventricular tachycardia (VT) and VF were obtained from patients during cardiac surgery. Segments 4 sec long were selected from tapes and digitized at 200 Hz, then split into three groups (VT, VF regular waveform, and VF irregular waveform). The positive peak P(j) of the ACF was defined as the maximum value between two function zeros, and RPL(j) represents the relation between P(j) and twice their own standard error. Parameter TR(1) was defined as the relation between P(1) width and the time of occurrence. ACFs were computed for the entire sample; RPL(j), D(j) = RPL(j) - RPL(j + 1), and TR(1) were calculated for every record. The results indicate that: (A) If RPL(1) greater than 1.2 and (1.6 greater than or equal to RPL(2) greater than 1) and (RPL(3) greater than 0.6 and D(1) greater than 0), then consider VT; (B) If (1.2 greater than or equal to RPL(1) greater than or equal to 1) and (1 greater than or equal to RPL(2) greater than or equal to 0.9) and D(1) greater than 9, then consider VT or VF with very regular waveform; (C) If (RPL(1) less than or equal to 1.8 and RPL(2) less than 0.9) or (RPL(2) less than 1.5 and D(1) less than 0) or RPL(3) less than 0.6, then consider VF. When 0.3 less than TR(1) less than 0.8, the underlying arrhythmia is VF or VT, and when it is outside this range, it is likely to be a supraventricular rhythm.
Conclusions:
(A) RPL(j) parameters have a high specificity for discriminating between VT and VF. The method is reliable and simple. (B) The TR(1) parameter together with RPL(j) allow discrimination between supraventricular tachycardias and ventricular originated tachyarrhythmias. (C) Further analysis must be done using problem-oriented arrhythmias data bases.
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